University Libraries Presents: The Library AI Club is a welcoming space for students, faculty, and staff to explore AI in a supportive, low-pressure environment. Meeting every two weeks, the club features discussions, collaborative projects, guest speakers, and hands-on experiments. Join us to learn, share ideas, and engage with AI responsibly and creatively. We'd love to see you at an upcoming meeting!
Location: Melville Library, Scholarly Communication Seminar Room
The AI Community will be hosting our very first Datathon💡📊
Ready to turn data into groundbreaking insights? 🧠
Compete in our Datathon, where you'll analyze real-world data 📈 and share innovate solutions in these tracks:
🏫 Student Life
🌱 Environment & Sustainability
💉 Health & Wellness
💰 Finance & Economics
Whether you're a data pro or just starting out, this is your chance to network, learn, and win exciting prizes! 🏆🎉 Bring your creativity 🧩 collaborate with fellow students 🧑🤝🧑 and gain hands-on experience showcasing your analytical skills 💻
Submissions will be judged by professors 🧑🏫 so take this chance to impress them!
There will be free food ☕ and games 🎲 to fuel your brain and imagination! Don't miss out--register now and unleash the power of data! 🔥✨
Registration Form: https://forms.gle/
Time: Friday (4/4) 10:30am - 5pm ⏰
Location: Bauman Center 📍
AI for Conservation: AI and Humans Combating Extinction Together by Daniel I. Rubenstein of Princeton University
ABSTRACT: The state of our planet is not good. We have lost more than 60% of the world's wildlife. Stopping the decline remains a challenge, especially since acquiring appropriate knowledge is expensive, time consuming and risky. Visual observations following the fates of a few individuals was the currency of the realm. But GPS technology and now machine learning provide a non-invasive scalable alternative. Photographs, taken by field scientists, tourists, automated cameras and incidental photographers, are the most abundant source of data on wildlife today. Wildbook, a project of tech for conservation coordinated by a non-profit Wild Me, is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high-resolution information databases, enabling scientific inquiry, conservation and citizen science.
BIO: Dan Rubenstein is the Class of 1877 Professor of Zoology. He is currently Director of Princeton's Environmental Studies Program and is former Chair of Princeton University's Department of Ecology and Evolutionary Biology and Director of Princeton's Program in African Studies. He is a behavioral ecologist who studies how environmental variation and individual differences shape social behavior, social structure, sex
roles and the dynamics of populations. He has special interests in all species of wild horses, zebras and asses, and has done field work on them throughout the world identifying rules governing decision-making, the emergence of complex behavioral patterns and how these understandings influence their management
and conservation. In Kenya he also works with pastoral communities to develop and assess impacts of various grazing strategies on rangeland quality, wildlife use and livelihoods. He has also developed a scout program for gathering data on Grevy's zebras and created curricular modules for local schools to raise awareness about the plight of this endangered species. He engages people as 'Citizen Scientists' and has recently extended his work to measuring the effects of environmental change, including issues pertaining to the global commons
and changes wrought by management and by global warming, on behavior.
ABSTRACT: The state of our planet is not good. We have lost more than 60% of the world's wildlife. Stopping the decline remains a challenge, especially since acquiring appropriate knowledge is expensive, time consuming and risky. Visual observations following the fates of a few individuals was the currency of the realm. But GPS technology and now machine learning provide a non-invasive scalable alternative. Photographs, taken by field scientists, tourists, automated cameras and incidental photographers, are the most abundant source of data on wildlife today. Wildbook, a project of tech for conservation coordinated by a non-profit Wild Me, is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high-resolution information databases, enabling scientific inquiry, conservation and citizen science.
BIO: Dan Rubenstein is the Class of 1877 Professor of Zoology. He is currently Director of Princeton's Environmental Studies Program and is former Chair of Princeton University's Department of Ecology and Evolutionary Biology and Director of Princeton's Program in African Studies. He is a behavioral ecologist who studies how environmental variation and individual differences shape social behavior, social structure, sex
roles and the dynamics of populations. He has special interests in all species of wild horses, zebras and asses, and has done field work on them throughout the world identifying rules governing decision-making, the emergence of complex behavioral patterns and how these understandings influence their management
and conservation. In Kenya he also works with pastoral communities to develop and assess impacts of various grazing strategies on rangeland quality, wildlife use and livelihoods. He has also developed a scout program for gathering data on Grevy's zebras and created curricular modules for local schools to raise awareness about the plight of this endangered species. He engages people as 'Citizen Scientists' and has recently extended his work to measuring the effects of environmental change, including issues pertaining to the global commons
and changes wrought by management and by global warming, on behavior.
CSE 600 Seminar Series | Fall 2025
Speakers: Jessica Roman, Simone Binneman, Michalis Polychronakis
The 2025 Conference on Empirical Methods in Natural Language Processing will be held in Suzhou, China from November 5 to November 9. More information can be found here.
Abstract: Modern technologies enable enhanced integrity and privacy guarantees not just for data, but also for computation. This is perhaps most emphatically demonstrated by the steady rise of zero-knowledge proofs, which are short certificates that attest to the correctness of computations (e.g., an age verification check) without revealing any secret inputs (e.g., the birth date on a digital ID). This subtly powerful technology enables anonymous credentials, privacy-preserving machine learning, anonymous blockchains, and much more--making the question of efficient zero-knowledge proofs fundamental to modern secure systems. Echoing Moore's law for computing, zero-knowledge proofs have improved on this front by ten orders of magnitude in the last two decades. In this talk, I will discuss our work on overcoming a key bottleneck that has emerged in this development: memory efficiency.
Speaker: Abhiram Kothapalli is a postdoctoral scholar at the University of California, Berkeley, hosted by Sanjam Garg. He is a recent graduate of Carnegie Mellon University, where he earned his Ph.D. in Computer Science, advised by Bryan Parno. Previously, he was at the University of Illinois at Urbana-Champaign, where he earned his B.S. in Computer Science and B.S. in Mathematics. Kothapalli's research develops cryptographic techniques aimed at scaling expressive privacy and integrity guarantees across the internet.
Location: NCS 120
Speaker: Abhiram Kothapalli is a postdoctoral scholar at the University of California, Berkeley, hosted by Sanjam Garg. He is a recent graduate of Carnegie Mellon University, where he earned his Ph.D. in Computer Science, advised by Bryan Parno. Previously, he was at the University of Illinois at Urbana-Champaign, where he earned his B.S. in Computer Science and B.S. in Mathematics. Kothapalli's research develops cryptographic techniques aimed at scaling expressive privacy and integrity guarantees across the internet.
Location: NCS 120
Abstract: Artificial intelligence (AI) is rapidly transforming scientific discovery, enabling breakthroughs in areas ranging from drug discovery to modeling complex physical systems. In the life sciences, AI has traditionally been applied to prediction tasks such as classifying molecules as toxic or non-toxic, estimating drug properties, or solving partial differential equations. These discriminative models have proven powerful, but they are inherently limited to mapping existing inputs to deterministic outputs. A new wave of methods is shifting the paradigm from discrimination to generation: creating new possibilities, such as generating novel molecules or designing new drugs. By reframing AI as both a predictive and generative engine, this shift offers new pathways for accelerating discovery and innovation in life sciences at an unprecedented scale. This talk will cover several aspects of AI for Science (AI4Sci), beginning with advances in discriminative models for molecular systems and solving PDEs, and then turning to generative approaches, including diffusion models for 3D molecular generation and large language models for drug editing. Together, these developments illustrate how moving from prediction to creation is redefining what AI can contribute to science.
Bio: Wenhan Gao is a fourth-year Ph.D. student in Applied Mathematics under the supervision of Professor Yi Liu. He was also a Staff Research Scientist Intern at VISA Research, where he worked on large language models (LLMs) and multi-agent systems for commerce. Wenhan's research focuses on AI for Science (AI4Sci), with a particular emphasis on generative AI. His work looks deep into the fundamental mechanisms of AI models when applied to scientific tasks, and he strives to incorporate established scientific priors, such as symmetry, into model design. He has published papers as a first or corresponding author in leading AI and computational venues, including ICLR, ICML, NeurIPS, TMLR, ACL, and the Journal of Computational Physics. In addition to his research, Wenhan has served as a reviewer and oral session chair for top AI conferences and as a lecturer for both undergraduate and graduate courses at Stony Brook University.
Location: IACS Seminar Room or Zoom
This seminar will take place in person and online*
Join Zoom Meeting: https://stonybrook.zoom.us/j/
Meeting ID: 916 7009 3552
Passcode: 434045
Bio: Wenhan Gao is a fourth-year Ph.D. student in Applied Mathematics under the supervision of Professor Yi Liu. He was also a Staff Research Scientist Intern at VISA Research, where he worked on large language models (LLMs) and multi-agent systems for commerce. Wenhan's research focuses on AI for Science (AI4Sci), with a particular emphasis on generative AI. His work looks deep into the fundamental mechanisms of AI models when applied to scientific tasks, and he strives to incorporate established scientific priors, such as symmetry, into model design. He has published papers as a first or corresponding author in leading AI and computational venues, including ICLR, ICML, NeurIPS, TMLR, ACL, and the Journal of Computational Physics. In addition to his research, Wenhan has served as a reviewer and oral session chair for top AI conferences and as a lecturer for both undergraduate and graduate courses at Stony Brook University.
Location: IACS Seminar Room or Zoom
This seminar will take place in person and online*
Join Zoom Meeting: https://stonybrook.zoom.us/j/
Meeting ID: 916 7009 3552
Passcode: 434045
You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.
Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.
Abstract: The increasing complexity and volume of data from electron microscopy necessitates advanced computational tools for timely and accurate analysis. In this talk, I will present several machine learning (ML) models developed to interpret diverse datasets from transmission electron microscopy (TEM). First, I demonstrate segmentation models for labelling regions of interest from in situ TEM images, such as atomic column positions or reaction sites that allow atomic-level quantitative analysis of data. Second, I introduce a self-supervised CNN model for denoising of low-dose HRTEM images, enabling clearer visualization of atomic features without sacrificing temporal resolution. Finally, a transformer-based model trained to predict copper oxidation states directly from their electron energy loss spectroscopy spectra will be introduced. Together, these projects showcase the power of tailored ML solutions to extract quantitative insights from complex microscopy data.
Biography: Brian Lee is a research associate working for the Electron Microscopy group and Theory and Computation group at the Center for Functional Nanomaterials. Previously, he has received PhD in Mechanical Engineering from Duke University and worked as a postdoc at Purdue University. His research focuses on applying machine learning and simulation techniques for materials science.
Location: CDS, Bldg. 725, Training Room
Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1604383624?pwd=ffQ5cUPNxTI7nzClKQO6cnsNbhF9Vf.1
Meeting ID: 160 438 3624
Passcode: 558449
Abstract: Jailbreak attacks circumvent LLMs' built-in safeguards by concealing harmful queries within adversarial prompts. While most existing defenses attempt to mitigate the effects of adversarial prompts, they often prove inadequate as adversarial prompts can take arbitrary, adaptive forms. This paper introduces RobustKV, a novel jailbreak defense that takes a fundamentally different approach by selectively removing critical tokens of harmful queries from key-value (KV) caches. Intuitively, for an adversarial prompt to be effective, its tokens must achieve sufficient `importance' (measured by attention scores), which consequently lowers the importance of tokens in the concealed harmful query. Therefore, by carefully evicting the KVs of low-ranked tokens, RobustKV minimizes the harmful query's presence in the KV cache, thus preventing the LLM from generating informative responses. Extensive evaluation using benchmark datasets and models demonstrates that RobustKV effectively counters state-of-the-art jailbreak attacks while maintaining the LLM's performance on benign queries. Notably, RobustKV creates an interesting effectiveness-evasiveness dilemma for the adversary, leading to its robustness against adaptive attacks.
Speaker: Tanqiu Jiang
Where: NCS 220 and Zoom (https://stonybrook.zoom.us/j/
Speaker: Tanqiu Jiang
Where: NCS 220 and Zoom (https://stonybrook.zoom.us/j/
Looking to learn about a new topic or skill? Look no further! Gemini's Guided Learning feature acts as your own personal tutor, teaching you about a particular subject through an engaging back and forth conversation. This AI tool helps users develop their knowledge and skills on a wide variety of topics, acting as a patient mentor, breaking down complex topics step-by-step. This session will take place on 2/24 at 11 AM. Please register using the link below!
https://stonybrookuniversity.co1.qualtrics.com/jfe/form/SV_a9PVlBw0E1Bal1A?
https://stonybrookuniversity.co1.qualtrics.com/jfe/form/SV_a9PVlBw0E1Bal1A?